stagehandvsbrowser-use
Stagehand is an AI-powered browser automation framework for JavaScript and TypeScript, built by Browserbase. It provides a simple API for controlling browsers using natural language instructions, powered by large language models.
Stagehand offers three core primitives:
- act()
Performs actions on the page described in natural language. For example,
page.act("click the login button")will find and click the appropriate element. - extract() Extracts structured data from the page based on a natural language description and an optional schema definition.
- observe() Analyzes the current page state and returns actionable elements and their descriptions, useful for understanding what actions are available on a page.
Key features include:
- TypeScript-first Built with full TypeScript support and type-safe extraction using Zod schemas.
- Multiple LLM providers Works with OpenAI, Anthropic, and other LLM providers for powering the AI.
- Vision and DOM analysis Combines visual screenshot analysis with DOM inspection for robust element identification.
- Playwright integration Uses Playwright as the browser automation backend, giving access to the full Playwright API alongside AI-powered actions.
- Browserbase cloud Optionally integrates with Browserbase cloud for managed browser infrastructure.
Stagehand is particularly suited for automating complex web workflows where traditional selectors would be fragile, such as interacting with frequently changing UIs or scraping sites with dynamic layouts.
Browser-use is a Python library that enables AI agents to control web browsers using natural language instructions. It connects large language models (LLMs) to browser automation, allowing you to describe what you want done in plain English instead of writing explicit selectors and interaction code.
Key features include:
- Natural language browser control Describe tasks like "go to Amazon and find the cheapest laptop under $500" and the AI agent will navigate, interact with elements, and extract the requested information.
- Multi-step task execution Can handle complex workflows that require multiple pages, form filling, clicking, scrolling, and waiting for dynamic content.
- Vision support Uses screenshot analysis (multimodal LLMs) to understand page layout and find elements visually, not just through DOM inspection.
- Multiple LLM providers Works with OpenAI, Anthropic Claude, Google Gemini, and other LLM providers.
- Playwright backend Uses Playwright under the hood for reliable browser automation across Chrome, Firefox, and Safari.
- Structured output Can return extracted data in structured formats defined by Pydantic models.
Browser-use represents a new paradigm in web scraping where instead of writing brittle selectors, you describe the extraction task and let the AI figure out how to navigate and extract the data. This is especially useful for scraping diverse sites with varying layouts.